Overview

Dataset statistics

Number of variables93
Number of observations591
Missing cells22495
Missing cells (%)40.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory397.2 KiB
Average record size in memory688.2 B

Variable types

Categorical20
DateTime1
Boolean46
Numeric26

Warnings

study_no has a high cardinality: 75 distinct values High cardinality
medication_list has a high cardinality: 90 distinct values High cardinality
abdominal_pain_level is highly correlated with albumin and 2 other fieldsHigh correlation
albumin is highly correlated with abdominal_pain_levelHigh correlation
haematocrit_percent is highly correlated with haemoglobin and 1 other fieldsHigh correlation
haemoglobin is highly correlated with haematocrit_percentHigh correlation
liver_size is highly correlated with abdominal_pain_levelHigh correlation
lymphocytes_percent is highly correlated with vomiting_levelHigh correlation
sbp is highly correlated with vomiting_levelHigh correlation
vomiting_level is highly correlated with abdominal_pain_level and 3 other fieldsHigh correlation
day_from_enrolment is highly correlated with day_from_admissionHigh correlation
day_from_admission is highly correlated with day_from_enrolmentHigh correlation
abdominal_tenderness has 266 (45.0%) missing values Missing
albumin has 527 (89.2%) missing values Missing
alt has 510 (86.3%) missing values Missing
antibiotics has 337 (57.0%) missing values Missing
antibiotics_list has 549 (92.9%) missing values Missing
ast has 510 (86.3%) missing values Missing
bleeding has 302 (51.1%) missing values Missing
bleeding_vaginal has 8 (1.4%) missing values Missing
blood_fluid has 444 (75.1%) missing values Missing
body_temperature has 265 (44.8%) missing values Missing
colloid has 445 (75.3%) missing values Missing
colloid_description has 581 (98.3%) missing values Missing
conjunctival_injection has 265 (44.8%) missing values Missing
coronary_heart_disease has 520 (88.0%) missing values Missing
creatine_kinase has 555 (93.9%) missing values Missing
creatinine has 509 (86.1%) missing values Missing
crystalloid has 444 (75.1%) missing values Missing
crystalloid_description has 450 (76.1%) missing values Missing
dbp has 319 (54.0%) missing values Missing
fluid_reason_other has 444 (75.1%) missing values Missing
fluid_reason_other_description has 577 (97.6%) missing values Missing
haematocrit_high has 444 (75.1%) missing values Missing
haematocrit_percent has 256 (43.3%) missing values Missing
haemoglobin has 265 (44.8%) missing values Missing
hematemesis has 264 (44.7%) missing values Missing
hypertension has 519 (87.8%) missing values Missing
igg has 463 (78.3%) missing values Missing
igg_interpretation has 463 (78.3%) missing values Missing
igm has 463 (78.3%) missing values Missing
igm_interpretation has 463 (78.3%) missing values Missing
lethargy has 264 (44.7%) missing values Missing
liver_palpation has 265 (44.8%) missing values Missing
lymphadenopathy has 266 (45.0%) missing values Missing
lymphadenopathy_specification has 566 (95.8%) missing values Missing
lymphocytes_percent has 256 (43.3%) missing values Missing
medication has 338 (57.2%) missing values Missing
medication_list has 454 (76.8%) missing values Missing
melaena has 264 (44.7%) missing values Missing
monocytes_percent has 256 (43.3%) missing values Missing
neutrophils_percent has 257 (43.5%) missing values Missing
parental_fluid_volume has 445 (75.3%) missing values Missing
pcr_dengue_load has 519 (87.8%) missing values Missing
pcr_dengue_serotype has 24 (4.1%) missing values Missing
pharyngeal_injection has 266 (45.0%) missing values Missing
platelets has 444 (75.1%) missing values Missing
plt has 256 (43.3%) missing values Missing
pulse has 269 (45.5%) missing values Missing
rales_crackles has 264 (44.7%) missing values Missing
rehydration has 444 (75.1%) missing values Missing
renal_disease has 519 (87.8%) missing values Missing
respiratory_rate has 266 (45.0%) missing values Missing
restlessness has 264 (44.7%) missing values Missing
rhinitis has 265 (44.8%) missing values Missing
sbp has 265 (44.8%) missing values Missing
serology_interpretation has 519 (87.8%) missing values Missing
shock_resucitation has 444 (75.1%) missing values Missing
skin_describe has 579 (98.0%) missing values Missing
treatment_haemorrhage has 444 (75.1%) missing values Missing
vomiting has 82 (13.9%) missing values Missing
wbc has 256 (43.3%) missing values Missing
pcr_dengue_load has 21 (3.6%) zeros Zeros
day_from_enrolment has 72 (12.2%) zeros Zeros
day_from_admission has 72 (12.2%) zeros Zeros

Reproduction

Analysis started2021-02-12 10:49:02.535065
Analysis finished2021-02-12 10:50:10.598467
Duration1 minute and 8.06 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Categorical

HIGH CARDINALITY

Distinct75
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
20-0708
 
12
20-0718
 
11
20-0707
 
11
20-0727
 
11
20-0705
 
11
Other values (70)
535 

Length

Max length9
Median length7
Mean length7.027072758
Min length7

Characters and Unicode

Total characters4153
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st row20 - 0762
2nd row20 - 0762
3rd row20 - 0762
4th row20 - 0762
5th row20 - 0762
ValueCountFrequency (%)
20-070812
 
2.0%
20-071811
 
1.9%
20-070711
 
1.9%
20-072711
 
1.9%
20-070511
 
1.9%
20-071411
 
1.9%
20-073711
 
1.9%
20-075110
 
1.7%
20-074810
 
1.7%
20-073310
 
1.7%
Other values (65)483
81.7%
2021-02-12T10:50:10.789041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20-070812
 
2.0%
20-071811
 
1.8%
20-072711
 
1.8%
20-070711
 
1.8%
20-073711
 
1.8%
20-071411
 
1.8%
20-070511
 
1.8%
20-076710
 
1.6%
20-073310
 
1.6%
20-074210
 
1.6%
Other values (67)499
82.2%

Most occurring characters

ValueCountFrequency (%)
01319
31.8%
2747
18.0%
7698
16.8%
-591
14.2%
1153
 
3.7%
3136
 
3.3%
6132
 
3.2%
5124
 
3.0%
4121
 
2.9%
865
 
1.6%
Other values (2)67
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3546
85.4%
Dash Punctuation591
 
14.2%
Space Separator16
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
01319
37.2%
2747
21.1%
7698
19.7%
1153
 
4.3%
3136
 
3.8%
6132
 
3.7%
5124
 
3.5%
4121
 
3.4%
865
 
1.8%
951
 
1.4%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
-591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4153
100.0%

Most frequent character per script

ValueCountFrequency (%)
01319
31.8%
2747
18.0%
7698
16.8%
-591
14.2%
1153
 
3.7%
3136
 
3.3%
6132
 
3.2%
5124
 
3.0%
4121
 
2.9%
865
 
1.6%
Other values (2)67
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4153
100.0%

Most frequent character per block

ValueCountFrequency (%)
01319
31.8%
2747
18.0%
7698
16.8%
-591
14.2%
1153
 
3.7%
3136
 
3.3%
6132
 
3.2%
5124
 
3.0%
4121
 
2.9%
865
 
1.6%
Other values (2)67
 
1.6%

date
Date

Distinct175
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum2013-06-15 00:00:00
Maximum2013-12-24 00:00:00
2021-02-12T10:50:10.897899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:50:11.014180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

abdominal_pain_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
567 
1.0
 
18
3.0
 
4
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan567
95.9%
1.018
 
3.0%
3.04
 
0.7%
2.02
 
0.3%
2021-02-12T10:50:11.205765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:11.259508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan567
95.9%
1.018
 
3.0%
3.04
 
0.7%
2.02
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n1134
64.0%
a567
32.0%
.24
 
1.4%
024
 
1.4%
118
 
1.0%
34
 
0.2%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1701
95.9%
Decimal Number48
 
2.7%
Other Punctuation24
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
024
50.0%
118
37.5%
34
 
8.3%
22
 
4.2%
ValueCountFrequency (%)
n1134
66.7%
a567
33.3%
ValueCountFrequency (%)
.24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1701
95.9%
Common72
 
4.1%

Most frequent character per script

ValueCountFrequency (%)
.24
33.3%
024
33.3%
118
25.0%
34
 
5.6%
22
 
2.8%
ValueCountFrequency (%)
n1134
66.7%
a567
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1134
64.0%
a567
32.0%
.24
 
1.4%
024
 
1.4%
118
 
1.0%
34
 
0.2%
22
 
0.1%

abdominal_tenderness
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing266
Missing (%)45.0%
Memory size4.7 KiB
False
291 
True
34 
(Missing)
266 
ValueCountFrequency (%)
False291
49.2%
True34
 
5.8%
(Missing)266
45.0%
2021-02-12T10:50:11.305446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

Distinct37
Distinct (%)6.3%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean29.98639456
Minimum5
Maximum64
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:11.374658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q120
median26
Q336
95-th percentile56
Maximum64
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.59388398
Coefficient of variation (CV)0.4533350602
Kurtosis-0.16922626
Mean29.98639456
Median Absolute Deviation (MAD)7
Skewness0.7861039106
Sum17632
Variance184.7936817
MonotocityNot monotonic
2021-02-12T10:50:11.483431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1951
 
8.6%
2434
 
5.8%
2932
 
5.4%
2232
 
5.4%
5329
 
4.9%
3127
 
4.6%
2627
 
4.6%
2326
 
4.4%
3425
 
4.2%
3624
 
4.1%
Other values (27)281
47.5%
ValueCountFrequency (%)
57
1.2%
99
1.5%
1112
2.0%
127
1.2%
1410
1.7%
ValueCountFrequency (%)
649
1.5%
5910
1.7%
589
1.5%
5611
1.9%
557
1.2%

albumin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct21
Distinct (%)32.8%
Missing527
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean40.96875
Minimum24
Maximum51
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:11.592644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile28
Q137.75
median42
Q346
95-th percentile50
Maximum51
Range27
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation6.480664159
Coefficient of variation (CV)0.1581855477
Kurtosis0.3350358426
Mean40.96875
Median Absolute Deviation (MAD)4
Skewness-0.8170171315
Sum2622
Variance41.99900794
MonotocityNot monotonic
2021-02-12T10:50:11.692715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
427
 
1.2%
476
 
1.0%
396
 
1.0%
435
 
0.8%
455
 
0.8%
464
 
0.7%
504
 
0.7%
483
 
0.5%
363
 
0.5%
343
 
0.5%
Other values (11)18
 
3.0%
(Missing)527
89.2%
ValueCountFrequency (%)
241
0.2%
252
0.3%
282
0.3%
311
0.2%
321
0.2%
ValueCountFrequency (%)
511
 
0.2%
504
0.7%
483
0.5%
476
1.0%
464
0.7%

alt
Real number (ℝ≥0)

MISSING

Distinct54
Distinct (%)66.7%
Missing510
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean61.35802469
Minimum8
Maximum437
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:11.798903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q129
median38
Q372
95-th percentile172
Maximum437
Range429
Interquartile range (IQR)43

Descriptive statistics

Standard deviation64.35221609
Coefficient of variation (CV)1.048798693
Kurtosis15.45445046
Mean61.35802469
Median Absolute Deviation (MAD)18
Skewness3.428885737
Sum4970
Variance4141.207716
MonotocityNot monotonic
2021-02-12T10:50:11.941391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
345
 
0.8%
204
 
0.7%
304
 
0.7%
384
 
0.7%
294
 
0.7%
173
 
0.5%
692
 
0.3%
472
 
0.3%
262
 
0.3%
122
 
0.3%
Other values (44)49
 
8.3%
(Missing)510
86.3%
ValueCountFrequency (%)
81
 
0.2%
122
0.3%
131
 
0.2%
151
 
0.2%
173
0.5%
ValueCountFrequency (%)
4371
0.2%
2891
0.2%
1981
0.2%
1801
0.2%
1721
0.2%

anemia
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
575 
True
 
16
ValueCountFrequency (%)
False575
97.3%
True16
 
2.7%
2021-02-12T10:50:12.018125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

anorexia
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
True
516 
False
75 
ValueCountFrequency (%)
True516
87.3%
False75
 
12.7%
2021-02-12T10:50:12.060907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antibiotics
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing337
Missing (%)57.0%
Memory size4.7 KiB
False
212 
True
42 
(Missing)
337 
ValueCountFrequency (%)
False212
35.9%
True42
 
7.1%
(Missing)337
57.0%
2021-02-12T10:50:12.103734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antibiotics_list
Categorical

MISSING

Distinct17
Distinct (%)40.5%
Missing549
Missing (%)92.9%
Memory size4.7 KiB
AZITHROMYCIN
6 
CEFOPERAZONE+ SUNBACTAM
4 
AMPICILIN+SULBACTAM
4 
CEFTAZIDIME
4 
ROCEPHIN
4 
Other values (12)
20 

Length

Max length36
Median length12
Mean length16.11904762
Min length8

Characters and Unicode

Total characters677
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)16.7%

Sample

1st rowCEFTRYAXON, AZITHROMYCIN,DOXYCYCLIN
2nd rowCEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN
3rd rowCEFTRIAXON, AZITHROMYCIN, DOXYCYLIN
4th rowCEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN
5th rowCEFTRIAXON
ValueCountFrequency (%)
AZITHROMYCIN6
 
1.0%
CEFOPERAZONE+ SUNBACTAM4
 
0.7%
AMPICILIN+SULBACTAM4
 
0.7%
CEFTAZIDIME4
 
0.7%
ROCEPHIN4
 
0.7%
SAVIAZIT3
 
0.5%
CEFTRIAXONE, DOXYCYCLIN3
 
0.5%
AZITROMYCIN3
 
0.5%
CEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN2
 
0.3%
ERTAPENEM2
 
0.3%
Other values (7)7
 
1.2%
(Missing)549
92.9%
2021-02-12T10:50:12.291034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
azithromycin9
15.5%
doxycyclin6
10.3%
sunbactam4
 
6.9%
ampicilin+sulbactam4
 
6.9%
cefoperazone4
 
6.9%
rocephin4
 
6.9%
ceftazidime4
 
6.9%
ceftriaxon4
 
6.9%
azitromycin3
 
5.2%
ceftriaxone3
 
5.2%
Other values (10)13
22.4%

Most occurring characters

ValueCountFrequency (%)
I75
 
11.1%
C63
 
9.3%
A62
 
9.2%
N49
 
7.2%
E47
 
6.9%
O45
 
6.6%
T40
 
5.9%
R34
 
5.0%
M33
 
4.9%
Y30
 
4.4%
Other values (14)199
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter640
94.5%
Space Separator16
 
2.4%
Other Punctuation13
 
1.9%
Math Symbol8
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
I75
11.7%
C63
 
9.8%
A62
 
9.7%
N49
 
7.7%
E47
 
7.3%
O45
 
7.0%
T40
 
6.2%
R34
 
5.3%
M33
 
5.2%
Y30
 
4.7%
Other values (11)162
25.3%
ValueCountFrequency (%)
,13
100.0%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin640
94.5%
Common37
 
5.5%

Most frequent character per script

ValueCountFrequency (%)
I75
11.7%
C63
 
9.8%
A62
 
9.7%
N49
 
7.7%
E47
 
7.3%
O45
 
7.0%
T40
 
6.2%
R34
 
5.3%
M33
 
5.2%
Y30
 
4.7%
Other values (11)162
25.3%
ValueCountFrequency (%)
16
43.2%
,13
35.1%
+8
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII677
100.0%

Most frequent character per block

ValueCountFrequency (%)
I75
 
11.1%
C63
 
9.3%
A62
 
9.2%
N49
 
7.2%
E47
 
6.9%
O45
 
6.6%
T40
 
5.9%
R34
 
5.0%
M33
 
4.9%
Y30
 
4.4%
Other values (14)199
29.4%

ascites
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
553 
True
 
35
(Missing)
 
3
ValueCountFrequency (%)
False553
93.6%
True35
 
5.9%
(Missing)3
 
0.5%
2021-02-12T10:50:12.354828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

MISSING

Distinct58
Distinct (%)71.6%
Missing510
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean85.7654321
Minimum17
Maximum831
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:12.440626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20
Q126
median42
Q376
95-th percentile352
Maximum831
Range814
Interquartile range (IQR)50

Descriptive statistics

Standard deviation129.1645531
Coefficient of variation (CV)1.506021132
Kurtosis17.30934205
Mean85.7654321
Median Absolute Deviation (MAD)19
Skewness3.887441517
Sum6947
Variance16683.48179
MonotocityNot monotonic
2021-02-12T10:50:12.584472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265
 
0.8%
573
 
0.5%
323
 
0.5%
243
 
0.5%
252
 
0.3%
222
 
0.3%
462
 
0.3%
342
 
0.3%
212
 
0.3%
292
 
0.3%
Other values (48)55
 
9.3%
(Missing)510
86.3%
ValueCountFrequency (%)
171
0.2%
181
0.2%
191
0.2%
202
0.3%
212
0.3%
ValueCountFrequency (%)
8311
0.2%
6301
0.2%
4081
0.2%
3901
0.2%
3521
0.2%

asthma
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
574 
True
 
17
ValueCountFrequency (%)
False574
97.1%
True17
 
2.9%
2021-02-12T10:50:12.657556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding
Boolean

MISSING

Distinct2
Distinct (%)0.7%
Missing302
Missing (%)51.1%
Memory size4.7 KiB
True
212 
False
77 
(Missing)
302 
ValueCountFrequency (%)
True212
35.9%
False77
 
13.0%
(Missing)302
51.1%
2021-02-12T10:50:12.695858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gum
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
549 
True
 
39
(Missing)
 
3
ValueCountFrequency (%)
False549
92.9%
True39
 
6.6%
(Missing)3
 
0.5%
2021-02-12T10:50:12.734328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_nose
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
574 
True
 
14
(Missing)
 
3
ValueCountFrequency (%)
False574
97.1%
True14
 
2.4%
(Missing)3
 
0.5%
2021-02-12T10:50:12.769559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vaginal
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing8
Missing (%)1.4%
Memory size4.7 KiB
False
547 
True
 
36
(Missing)
 
8
ValueCountFrequency (%)
False547
92.6%
True36
 
6.1%
(Missing)8
 
1.4%
2021-02-12T10:50:12.807256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

blood_fluid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
146 
True
 
1
(Missing)
444 
ValueCountFrequency (%)
False146
 
24.7%
True1
 
0.2%
(Missing)444
75.1%
2021-02-12T10:50:12.844209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)13.8%
Missing265
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean37.31717791
Minimum35.3
Maximum40
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:12.922811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum35.3
5-th percentile36
Q136.5
median37
Q338.2
95-th percentile39.5
Maximum40
Range4.7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.128789418
Coefficient of variation (CV)0.03024852041
Kurtosis-0.6945074977
Mean37.31717791
Median Absolute Deviation (MAD)0.7
Skewness0.676671306
Sum12165.4
Variance1.27416555
MonotocityNot monotonic
2021-02-12T10:50:13.056121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36.538
 
6.4%
3728
 
4.7%
36.825
 
4.2%
3920
 
3.4%
3619
 
3.2%
36.217
 
2.9%
36.413
 
2.2%
39.512
 
2.0%
3811
 
1.9%
36.711
 
1.9%
Other values (35)132
22.3%
(Missing)265
44.8%
ValueCountFrequency (%)
35.32
 
0.3%
35.51
 
0.2%
35.61
 
0.2%
35.85
 
0.8%
3619
3.2%
ValueCountFrequency (%)
402
0.3%
39.91
 
0.2%
39.84
0.7%
39.71
 
0.2%
39.62
0.3%

bruising
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
True
313 
False
275 
(Missing)
 
3
ValueCountFrequency (%)
True313
53.0%
False275
46.5%
(Missing)3
 
0.5%
2021-02-12T10:50:13.129028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

chronic_hepatitis
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
580 
True
 
11
ValueCountFrequency (%)
False580
98.1%
True11
 
1.9%
2021-02-12T10:50:13.166537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

colloid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing445
Missing (%)75.3%
Memory size4.7 KiB
False
135 
True
 
11
(Missing)
445 
ValueCountFrequency (%)
False135
 
22.8%
True11
 
1.9%
(Missing)445
75.3%
2021-02-12T10:50:13.205088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

colloid_description
Categorical

MISSING

Distinct4
Distinct (%)40.0%
Missing581
Missing (%)98.3%
Memory size4.7 KiB
D60
5 
ALBUMIN
3 
REFORTAN 6%
1 
D60, ALBUMIN
1 

Length

Max length12
Median length5
Mean length5.9
Min length3

Characters and Unicode

Total characters59
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st rowALBUMIN
2nd rowALBUMIN
3rd rowALBUMIN
4th rowREFORTAN 6%
5th rowD60, ALBUMIN
ValueCountFrequency (%)
D605
 
0.8%
ALBUMIN3
 
0.5%
REFORTAN 6%1
 
0.2%
D60, ALBUMIN1
 
0.2%
(Missing)581
98.3%
2021-02-12T10:50:13.400871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:13.468135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
d606
50.0%
albumin4
33.3%
61
 
8.3%
refortan1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
67
11.9%
D6
10.2%
06
10.2%
A5
8.5%
N5
8.5%
L4
 
6.8%
B4
 
6.8%
U4
 
6.8%
M4
 
6.8%
I4
 
6.8%
Other values (8)10
16.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42
71.2%
Decimal Number13
 
22.0%
Space Separator2
 
3.4%
Other Punctuation2
 
3.4%

Most frequent character per category

ValueCountFrequency (%)
D6
14.3%
A5
11.9%
N5
11.9%
L4
9.5%
B4
9.5%
U4
9.5%
M4
9.5%
I4
9.5%
R2
 
4.8%
E1
 
2.4%
Other values (3)3
7.1%
ValueCountFrequency (%)
67
53.8%
06
46.2%
ValueCountFrequency (%)
%1
50.0%
,1
50.0%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42
71.2%
Common17
28.8%

Most frequent character per script

ValueCountFrequency (%)
D6
14.3%
A5
11.9%
N5
11.9%
L4
9.5%
B4
9.5%
U4
9.5%
M4
9.5%
I4
9.5%
R2
 
4.8%
E1
 
2.4%
Other values (3)3
7.1%
ValueCountFrequency (%)
67
41.2%
06
35.3%
2
 
11.8%
%1
 
5.9%
,1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII59
100.0%

Most frequent character per block

ValueCountFrequency (%)
67
11.9%
D6
10.2%
06
10.2%
A5
8.5%
N5
8.5%
L4
 
6.8%
B4
 
6.8%
U4
 
6.8%
M4
 
6.8%
I4
 
6.8%
Other values (8)10
16.9%

conjunctival_injection
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing265
Missing (%)44.8%
Memory size4.7 KiB
False
263 
True
63 
(Missing)
265 
ValueCountFrequency (%)
False263
44.5%
True63
 
10.7%
(Missing)265
44.8%
2021-02-12T10:50:13.513998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

coronary_heart_disease
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing520
Missing (%)88.0%
Memory size4.7 KiB
False
70 
True
 
1
(Missing)
520 
ValueCountFrequency (%)
False70
 
11.8%
True1
 
0.2%
(Missing)520
88.0%
2021-02-12T10:50:13.552253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatine_kinase
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)91.7%
Missing555
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean83.11111111
Minimum27
Maximum543
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:13.617756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile30
Q142.75
median57.5
Q375.75
95-th percentile199.25
Maximum543
Range516
Interquartile range (IQR)33

Descriptive statistics

Standard deviation104.4051936
Coefficient of variation (CV)1.256212222
Kurtosis14.63012228
Mean83.11111111
Median Absolute Deviation (MAD)17
Skewness3.848977529
Sum2992
Variance10900.44444
MonotocityNot monotonic
2021-02-12T10:50:13.729970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
272
 
0.3%
582
 
0.3%
352
 
0.3%
951
 
0.2%
471
 
0.2%
321
 
0.2%
421
 
0.2%
811
 
0.2%
751
 
0.2%
691
 
0.2%
Other values (23)23
 
3.9%
(Missing)555
93.9%
ValueCountFrequency (%)
272
0.3%
311
0.2%
321
0.2%
352
0.3%
371
0.2%
ValueCountFrequency (%)
5431
0.2%
4491
0.2%
1161
0.2%
1001
0.2%
951
0.2%

creatinine
Real number (ℝ≥0)

MISSING

Distinct47
Distinct (%)57.3%
Missing509
Missing (%)86.1%
Infinite0
Infinite (%)0.0%
Mean85.06097561
Minimum32
Maximum446
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:13.850839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile51.1
Q163
median74
Q389.25
95-th percentile112.9
Maximum446
Range414
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation59.23573904
Coefficient of variation (CV)0.696391484
Kurtosis29.51792775
Mean85.06097561
Median Absolute Deviation (MAD)12
Skewness5.21063045
Sum6975
Variance3508.872779
MonotocityNot monotonic
2021-02-12T10:50:13.980732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1065
 
0.8%
784
 
0.7%
684
 
0.7%
744
 
0.7%
533
 
0.5%
633
 
0.5%
753
 
0.5%
613
 
0.5%
802
 
0.3%
962
 
0.3%
Other values (37)49
 
8.3%
(Missing)509
86.1%
ValueCountFrequency (%)
321
0.2%
391
0.2%
471
0.2%
501
0.2%
511
0.2%
ValueCountFrequency (%)
4461
0.2%
4241
0.2%
1441
0.2%
1361
0.2%
1131
0.2%

crystalloid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
True
141 
False
 
6
(Missing)
444 
ValueCountFrequency (%)
True141
 
23.9%
False6
 
1.0%
(Missing)444
75.1%
2021-02-12T10:50:14.055186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

crystalloid_description
Categorical

MISSING

Distinct15
Distinct (%)10.6%
Missing450
Missing (%)76.1%
Memory size4.7 KiB
RL
99 
NS
14 
RL,NS
 
6
NS, GLUCOSE 5%
 
6
DEXTROSE-NATRI
 
4
Other values (10)
12 

Length

Max length15
Median length2
Mean length3.659574468
Min length2

Characters and Unicode

Total characters516
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)5.7%

Sample

1st rowNS
2nd rowNS
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
RL99
 
16.8%
NS14
 
2.4%
RL,NS6
 
1.0%
NS, GLUCOSE 5%6
 
1.0%
DEXTROSE-NATRI4
 
0.7%
NS,RL2
 
0.3%
NS,GLUCOSE 5%2
 
0.3%
NS, RL1
 
0.2%
NS,GLUCOSE5%1
 
0.2%
NACL1
 
0.2%
Other values (5)5
 
0.8%
(Missing)450
76.1%
2021-02-12T10:50:14.413482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rl102
63.4%
ns21
 
13.0%
510
 
6.2%
glucose10
 
6.2%
rl,ns6
 
3.7%
dextrose-natri4
 
2.5%
ns,rl2
 
1.2%
ns,glucose2
 
1.2%
ns,glucose51
 
0.6%
ringer1
 
0.6%
Other values (2)2
 
1.2%

Most occurring characters

ValueCountFrequency (%)
L125
24.2%
R122
23.6%
S49
 
9.5%
N39
 
7.6%
E23
 
4.5%
,21
 
4.1%
20
 
3.9%
O17
 
3.3%
G15
 
2.9%
C15
 
2.9%
Other values (9)70
13.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter449
87.0%
Other Punctuation32
 
6.2%
Space Separator20
 
3.9%
Decimal Number11
 
2.1%
Dash Punctuation4
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
L125
27.8%
R122
27.2%
S49
 
10.9%
N39
 
8.7%
E23
 
5.1%
O17
 
3.8%
G15
 
3.3%
C15
 
3.3%
U13
 
2.9%
T10
 
2.2%
Other values (4)21
 
4.7%
ValueCountFrequency (%)
,21
65.6%
%11
34.4%
ValueCountFrequency (%)
20
100.0%
ValueCountFrequency (%)
511
100.0%
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin449
87.0%
Common67
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
L125
27.8%
R122
27.2%
S49
 
10.9%
N39
 
8.7%
E23
 
5.1%
O17
 
3.8%
G15
 
3.3%
C15
 
3.3%
U13
 
2.9%
T10
 
2.2%
Other values (4)21
 
4.7%
ValueCountFrequency (%)
,21
31.3%
20
29.9%
511
16.4%
%11
16.4%
-4
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII516
100.0%

Most frequent character per block

ValueCountFrequency (%)
L125
24.2%
R122
23.6%
S49
 
9.5%
N39
 
7.6%
E23
 
4.5%
,21
 
4.1%
20
 
3.9%
O17
 
3.3%
G15
 
2.9%
C15
 
2.9%
Other values (9)70
13.6%

dbp
Real number (ℝ≥0)

MISSING

Distinct39
Distinct (%)14.3%
Missing319
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean67.98529412
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:14.546600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52.55
Q160
median65
Q370
95-th percentile90
Maximum100
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.20324498
Coefficient of variation (CV)0.1647892404
Kurtosis1.012262986
Mean67.98529412
Median Absolute Deviation (MAD)5
Skewness1.079410189
Sum18492
Variance125.5126981
MonotocityNot monotonic
2021-02-12T10:50:14.666507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
6087
 
14.7%
7062
 
10.5%
8016
 
2.7%
6513
 
2.2%
5010
 
1.7%
1008
 
1.4%
626
 
1.0%
906
 
1.0%
824
 
0.7%
734
 
0.7%
Other values (29)56
 
9.5%
(Missing)319
54.0%
ValueCountFrequency (%)
5010
1.7%
511
 
0.2%
523
 
0.5%
531
 
0.2%
553
 
0.5%
ValueCountFrequency (%)
1008
1.4%
991
 
0.2%
982
 
0.3%
962
 
0.3%
906
1.0%

diabetes
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
576 
True
 
15
ValueCountFrequency (%)
False576
97.5%
True15
 
2.5%
2021-02-12T10:50:14.735651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fluid_reason_other
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
134 
True
 
13
(Missing)
444 
ValueCountFrequency (%)
False134
 
22.7%
True13
 
2.2%
(Missing)444
75.1%
2021-02-12T10:50:14.775742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fluid_reason_other_description
Categorical

MISSING

Distinct7
Distinct (%)50.0%
Missing577
Missing (%)97.6%
Memory size4.7 KiB
PHASE TRANSFER OF ANTIBIOTIC
4 
DIARRHEA
3 
SICK PATIENTS
2 
SOLVENT ANTIBIOTIC
2 
DECREASED PLATELETS
1 
Other values (2)
2 

Length

Max length28
Median length18
Mean length17.28571429
Min length7

Characters and Unicode

Total characters242
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)21.4%

Sample

1st rowSOLVENT ANTIBIOTIC
2nd rowSOLVENT ANTIBIOTIC
3rd rowALBUMIN
4th rowDECREASED PLATELETS
5th rowPHASE TRANSFER OF ANTIBIOTIC
ValueCountFrequency (%)
PHASE TRANSFER OF ANTIBIOTIC4
 
0.7%
DIARRHEA3
 
0.5%
SICK PATIENTS2
 
0.3%
SOLVENT ANTIBIOTIC2
 
0.3%
DECREASED PLATELETS1
 
0.2%
TRADITIONAL FORTEC1
 
0.2%
ALBUMIN1
 
0.2%
(Missing)577
97.6%
2021-02-12T10:50:14.950267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:15.020707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
antibiotic6
18.8%
phase4
12.5%
transfer4
12.5%
of4
12.5%
diarrhea3
9.4%
sick2
 
6.2%
solvent2
 
6.2%
patients2
 
6.2%
fortec1
 
3.1%
decreased1
 
3.1%
Other values (3)3
9.4%

Most occurring characters

ValueCountFrequency (%)
I28
11.6%
T27
11.2%
A27
11.2%
E21
8.7%
18
 
7.4%
R17
 
7.0%
S16
 
6.6%
N16
 
6.6%
O14
 
5.8%
C10
 
4.1%
Other values (10)48
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter224
92.6%
Space Separator18
 
7.4%

Most frequent character per category

ValueCountFrequency (%)
I28
12.5%
T27
12.1%
A27
12.1%
E21
9.4%
R17
7.6%
S16
7.1%
N16
7.1%
O14
 
6.2%
C10
 
4.5%
F9
 
4.0%
Other values (9)39
17.4%
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224
92.6%
Common18
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
I28
12.5%
T27
12.1%
A27
12.1%
E21
9.4%
R17
7.6%
S16
7.1%
N16
7.1%
O14
 
6.2%
C10
 
4.5%
F9
 
4.0%
Other values (9)39
17.4%
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII242
100.0%

Most frequent character per block

ValueCountFrequency (%)
I28
11.6%
T27
11.2%
A27
11.2%
E21
8.7%
18
 
7.4%
R17
 
7.0%
S16
 
6.6%
N16
 
6.6%
O14
 
5.8%
C10
 
4.1%
Other values (10)48
19.8%

gender
Categorical

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
Male
311 
Female
277 

Length

Max length6
Median length4
Mean length4.942176871
Min length4

Characters and Unicode

Total characters2906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Male311
52.6%
Female277
46.9%
(Missing)3
 
0.5%
2021-02-12T10:50:15.208691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:15.268955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
male311
52.9%
female277
47.1%

Most occurring characters

ValueCountFrequency (%)
e865
29.8%
a588
20.2%
l588
20.2%
M311
 
10.7%
F277
 
9.5%
m277
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2318
79.8%
Uppercase Letter588
 
20.2%

Most frequent character per category

ValueCountFrequency (%)
e865
37.3%
a588
25.4%
l588
25.4%
m277
 
11.9%
ValueCountFrequency (%)
M311
52.9%
F277
47.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2906
100.0%

Most frequent character per script

ValueCountFrequency (%)
e865
29.8%
a588
20.2%
l588
20.2%
M311
 
10.7%
F277
 
9.5%
m277
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2906
100.0%

Most frequent character per block

ValueCountFrequency (%)
e865
29.8%
a588
20.2%
l588
20.2%
M311
 
10.7%
F277
 
9.5%
m277
 
9.5%

haematocrit_high
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
121 
True
 
26
(Missing)
444 
ValueCountFrequency (%)
False121
 
20.5%
True26
 
4.4%
(Missing)444
75.1%
2021-02-12T10:50:15.302809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct166
Distinct (%)49.6%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean40.53134328
Minimum13.85
Maximum54.5
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:15.382858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13.85
5-th percentile33.14
Q137.3
median40.4
Q343.75
95-th percentile47.425
Maximum54.5
Range40.65
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation4.78043696
Coefficient of variation (CV)0.1179442025
Kurtosis2.313238949
Mean40.53134328
Median Absolute Deviation (MAD)3.2
Skewness-0.3381302229
Sum13578
Variance22.85257753
MonotocityNot monotonic
2021-02-12T10:50:15.497524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.18
 
1.4%
41.67
 
1.2%
39.86
 
1.0%
43.56
 
1.0%
42.45
 
0.8%
35.55
 
0.8%
425
 
0.8%
39.15
 
0.8%
40.75
 
0.8%
404
 
0.7%
Other values (156)279
47.2%
(Missing)256
43.3%
ValueCountFrequency (%)
13.851
0.2%
29.81
0.2%
30.61
0.2%
31.11
0.2%
31.31
0.2%
ValueCountFrequency (%)
54.51
0.2%
541
0.2%
53.11
0.2%
52.151
0.2%
51.61
0.2%

haemoglobin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct76
Distinct (%)23.3%
Missing265
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean13.7196319
Minimum9.3
Maximum18.5
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:15.616731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile10.9
Q112.4
median13.7
Q315
95-th percentile16.3
Maximum18.5
Range9.2
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.738110695
Coefficient of variation (CV)0.1266878519
Kurtosis-0.5064616875
Mean13.7196319
Median Absolute Deviation (MAD)1.3
Skewness-0.01823765764
Sum4472.6
Variance3.021028787
MonotocityNot monotonic
2021-02-12T10:50:15.747651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.912
 
2.0%
12.412
 
2.0%
12.911
 
1.9%
15.411
 
1.9%
13.210
 
1.7%
11.710
 
1.7%
14.810
 
1.7%
159
 
1.5%
11.98
 
1.4%
13.87
 
1.2%
Other values (66)226
38.2%
(Missing)265
44.8%
ValueCountFrequency (%)
9.31
 
0.2%
9.61
 
0.2%
101
 
0.2%
10.11
 
0.2%
10.23
0.5%
ValueCountFrequency (%)
18.51
0.2%
18.31
0.2%
17.62
0.3%
17.41
0.2%
17.21
0.2%

headache_level
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
527 
1.0
 
39
2.0
 
20
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan527
89.2%
1.039
 
6.6%
2.020
 
3.4%
3.05
 
0.8%
2021-02-12T10:50:15.943531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:16.008751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan527
89.2%
1.039
 
6.6%
2.020
 
3.4%
3.05
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
139
 
2.2%
220
 
1.1%
35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1581
89.2%
Decimal Number128
 
7.2%
Other Punctuation64
 
3.6%

Most frequent character per category

ValueCountFrequency (%)
064
50.0%
139
30.5%
220
 
15.6%
35
 
3.9%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%
ValueCountFrequency (%)
.64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1581
89.2%
Common192
 
10.8%

Most frequent character per script

ValueCountFrequency (%)
.64
33.3%
064
33.3%
139
20.3%
220
 
10.4%
35
 
2.6%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
139
 
2.2%
220
 
1.1%
35
 
0.3%

height
Real number (ℝ≥0)

Distinct30
Distinct (%)5.1%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean160.5969388
Minimum110
Maximum180
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:16.088355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile145
Q1154
median162
Q3168
95-th percentile180
Maximum180
Range70
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.2440387
Coefficient of variation (CV)0.07001402882
Kurtosis3.820133846
Mean160.5969388
Median Absolute Deviation (MAD)7
Skewness-1.152379402
Sum94431
Variance126.4284063
MonotocityNot monotonic
2021-02-12T10:50:16.207998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
16059
 
10.0%
16854
 
9.1%
17046
 
7.8%
15042
 
7.1%
15235
 
5.9%
18032
 
5.4%
16329
 
4.9%
15528
 
4.7%
16226
 
4.4%
15820
 
3.4%
Other values (20)217
36.7%
ValueCountFrequency (%)
1107
1.2%
1309
1.5%
14517
2.9%
1467
1.2%
14712
2.0%
ValueCountFrequency (%)
18032
5.4%
1798
 
1.4%
1768
 
1.4%
1748
 
1.4%
1729
 
1.5%

hematemesis
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
326 
True
 
1
(Missing)
264 
ValueCountFrequency (%)
False326
55.2%
True1
 
0.2%
(Missing)264
44.7%
2021-02-12T10:50:16.277896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hypertension
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing519
Missing (%)87.8%
Memory size4.7 KiB
False
69 
True
 
3
(Missing)
519 
ValueCountFrequency (%)
False69
 
11.7%
True3
 
0.5%
(Missing)519
87.8%
2021-02-12T10:50:16.315910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

igg
Real number (ℝ≥0)

MISSING

Distinct112
Distinct (%)87.5%
Missing463
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean17.35148438
Minimum0.78
Maximum28.21
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:16.400114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile1.23
Q17.2
median21.95
Q325.52
95-th percentile27.3855
Maximum28.21
Range27.43
Interquartile range (IQR)18.32

Descriptive statistics

Standard deviation9.52503978
Coefficient of variation (CV)0.5489466823
Kurtosis-1.163393544
Mean17.35148438
Median Absolute Deviation (MAD)4.57
Skewness-0.666192067
Sum2220.99
Variance90.72638282
MonotocityNot monotonic
2021-02-12T10:50:16.529578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.183
 
0.5%
27.083
 
0.5%
26.092
 
0.3%
222
 
0.3%
21.652
 
0.3%
22.882
 
0.3%
1.342
 
0.3%
272
 
0.3%
23.132
 
0.3%
1.232
 
0.3%
Other values (102)106
 
17.9%
(Missing)463
78.3%
ValueCountFrequency (%)
0.781
0.2%
0.791
0.2%
0.861
0.2%
1.031
0.2%
1.041
0.2%
ValueCountFrequency (%)
28.211
0.2%
28.121
0.2%
27.891
0.2%
27.881
0.2%
27.621
0.2%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)2.3%
Missing463
Missing (%)78.3%
Memory size4.7 KiB
Positive
64 
Negative
46 
Equivocal
18 

Length

Max length9
Median length8
Mean length8.140625
Min length8

Characters and Unicode

Total characters1042
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Positive64
 
10.8%
Negative46
 
7.8%
Equivocal18
 
3.0%
(Missing)463
78.3%
2021-02-12T10:50:16.720776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:16.774696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive64
50.0%
negative46
35.9%
equivocal18
 
14.1%

Most occurring characters

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter914
87.7%
Uppercase Letter128
 
12.3%

Most frequent character per category

ValueCountFrequency (%)
i192
21.0%
e156
17.1%
v128
14.0%
t110
12.0%
o82
9.0%
s64
 
7.0%
a64
 
7.0%
g46
 
5.0%
q18
 
2.0%
u18
 
2.0%
Other values (2)36
 
3.9%
ValueCountFrequency (%)
P64
50.0%
N46
35.9%
E18
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1042
100.0%

Most frequent character per script

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1042
100.0%

Most frequent character per block

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

igm
Real number (ℝ≥0)

MISSING

Distinct122
Distinct (%)95.3%
Missing463
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean25.87273438
Minimum5.27
Maximum45.71
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:16.862225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.27
5-th percentile6.3895
Q113.5725
median26.81
Q337.3275
95-th percentile45.0995
Maximum45.71
Range40.44
Interquartile range (IQR)23.755

Descriptive statistics

Standard deviation13.44181229
Coefficient of variation (CV)0.5195358208
Kurtosis-1.425192115
Mean25.87273438
Median Absolute Deviation (MAD)12.12
Skewness-0.05696820729
Sum3311.71
Variance180.6823177
MonotocityNot monotonic
2021-02-12T10:50:16.990579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.12
 
0.3%
26.812
 
0.3%
29.242
 
0.3%
45.682
 
0.3%
36.262
 
0.3%
45.082
 
0.3%
24.991
 
0.2%
25.461
 
0.2%
44.911
 
0.2%
12.031
 
0.2%
Other values (112)112
 
19.0%
(Missing)463
78.3%
ValueCountFrequency (%)
5.271
0.2%
5.561
0.2%
5.731
0.2%
6.12
0.3%
6.161
0.2%
ValueCountFrequency (%)
45.711
0.2%
45.682
0.3%
45.511
0.2%
45.341
0.2%
45.281
0.2%

igm_interpretation
Categorical

MISSING

Distinct3
Distinct (%)2.3%
Missing463
Missing (%)78.3%
Memory size4.7 KiB
Positive
103 
Negative
21 
Equivocal
 
4

Length

Max length9
Median length8
Mean length8.03125
Min length8

Characters and Unicode

Total characters1028
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowEquivocal
5th rowNegative
ValueCountFrequency (%)
Positive103
 
17.4%
Negative21
 
3.6%
Equivocal4
 
0.7%
(Missing)463
78.3%
2021-02-12T10:50:17.202551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:17.261593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive103
80.5%
negative21
 
16.4%
equivocal4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter900
87.5%
Uppercase Letter128
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
i231
25.7%
e145
16.1%
v128
14.2%
t124
13.8%
o107
11.9%
s103
11.4%
a25
 
2.8%
g21
 
2.3%
q4
 
0.4%
u4
 
0.4%
Other values (2)8
 
0.9%
ValueCountFrequency (%)
P103
80.5%
N21
 
16.4%
E4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1028
100.0%

Most frequent character per script

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

joint_pain_level
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
549 
1.0
 
32
2.0
 
8
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan549
92.9%
1.032
 
5.4%
2.08
 
1.4%
3.02
 
0.3%
2021-02-12T10:50:17.436826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:17.494306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan549
92.9%
1.032
 
5.4%
2.08
 
1.4%
3.02
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n1098
61.9%
a549
31.0%
.42
 
2.4%
042
 
2.4%
132
 
1.8%
28
 
0.5%
32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1647
92.9%
Decimal Number84
 
4.7%
Other Punctuation42
 
2.4%

Most frequent character per category

ValueCountFrequency (%)
042
50.0%
132
38.1%
28
 
9.5%
32
 
2.4%
ValueCountFrequency (%)
n1098
66.7%
a549
33.3%
ValueCountFrequency (%)
.42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1647
92.9%
Common126
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
.42
33.3%
042
33.3%
132
25.4%
28
 
6.3%
32
 
1.6%
ValueCountFrequency (%)
n1098
66.7%
a549
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1098
61.9%
a549
31.0%
.42
 
2.4%
042
 
2.4%
132
 
1.8%
28
 
0.5%
32
 
0.1%

lethargy
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
323 
True
 
4
(Missing)
264 
ValueCountFrequency (%)
False323
54.7%
True4
 
0.7%
(Missing)264
44.7%
2021-02-12T10:50:17.538765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_palpation
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing265
Missing (%)44.8%
Memory size4.7 KiB
False
310 
True
 
16
(Missing)
265 
ValueCountFrequency (%)
False310
52.5%
True16
 
2.7%
(Missing)265
44.8%
2021-02-12T10:50:17.577950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_size
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
575 
2.0
 
13
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan575
97.3%
2.013
 
2.2%
1.03
 
0.5%
2021-02-12T10:50:17.746926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:17.801991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan575
97.3%
2.013
 
2.2%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n1150
64.9%
a575
32.4%
.16
 
0.9%
016
 
0.9%
213
 
0.7%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1725
97.3%
Decimal Number32
 
1.8%
Other Punctuation16
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
016
50.0%
213
40.6%
13
 
9.4%
ValueCountFrequency (%)
n1150
66.7%
a575
33.3%
ValueCountFrequency (%)
.16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1725
97.3%
Common48
 
2.7%

Most frequent character per script

ValueCountFrequency (%)
.16
33.3%
016
33.3%
213
27.1%
13
 
6.2%
ValueCountFrequency (%)
n1150
66.7%
a575
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1150
64.9%
a575
32.4%
.16
 
0.9%
016
 
0.9%
213
 
0.7%
13
 
0.2%

lymphadenopathy
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing266
Missing (%)45.0%
Memory size4.7 KiB
False
300 
True
 
25
(Missing)
266 
ValueCountFrequency (%)
False300
50.8%
True25
 
4.2%
(Missing)266
45.0%
2021-02-12T10:50:17.842952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lymphadenopathy_specification
Categorical

MISSING

Distinct2
Distinct (%)8.0%
Missing566
Missing (%)95.8%
Memory size4.7 KiB
1.0
18 
Cervical
7 

Length

Max length8
Median length3
Mean length4.4
Min length3

Characters and Unicode

Total characters110
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCervical
2nd row1.0
3rd row1.0
4th rowCervical
5th row1.0
ValueCountFrequency (%)
1.018
 
3.0%
Cervical7
 
1.2%
(Missing)566
95.8%
2021-02-12T10:50:18.010122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:18.067122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.018
72.0%
cervical7
 
28.0%

Most occurring characters

ValueCountFrequency (%)
118
16.4%
.18
16.4%
018
16.4%
C7
 
6.4%
e7
 
6.4%
r7
 
6.4%
v7
 
6.4%
i7
 
6.4%
c7
 
6.4%
a7
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter49
44.5%
Decimal Number36
32.7%
Other Punctuation18
 
16.4%
Uppercase Letter7
 
6.4%

Most frequent character per category

ValueCountFrequency (%)
e7
14.3%
r7
14.3%
v7
14.3%
i7
14.3%
c7
14.3%
a7
14.3%
l7
14.3%
ValueCountFrequency (%)
118
50.0%
018
50.0%
ValueCountFrequency (%)
C7
100.0%
ValueCountFrequency (%)
.18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56
50.9%
Common54
49.1%

Most frequent character per script

ValueCountFrequency (%)
C7
12.5%
e7
12.5%
r7
12.5%
v7
12.5%
i7
12.5%
c7
12.5%
a7
12.5%
l7
12.5%
ValueCountFrequency (%)
118
33.3%
.18
33.3%
018
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ValueCountFrequency (%)
118
16.4%
.18
16.4%
018
16.4%
C7
 
6.4%
e7
 
6.4%
r7
 
6.4%
v7
 
6.4%
i7
 
6.4%
c7
 
6.4%
a7
 
6.4%

lymphocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct255
Distinct (%)76.1%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean40.9880597
Minimum4.1
Maximum77.7
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:18.157132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile14.67
Q129.15
median41.5
Q351.9
95-th percentile65.9
Maximum77.7
Range73.6
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation15.29407073
Coefficient of variation (CV)0.3731347822
Kurtosis-0.6521699417
Mean40.9880597
Median Absolute Deviation (MAD)11.4
Skewness-0.04490356914
Sum13731
Variance233.9085995
MonotocityNot monotonic
2021-02-12T10:50:18.293147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.74
 
0.7%
19.54
 
0.7%
383
 
0.5%
49.93
 
0.5%
323
 
0.5%
34.83
 
0.5%
43.13
 
0.5%
23.33
 
0.5%
52.53
 
0.5%
56.73
 
0.5%
Other values (245)303
51.3%
(Missing)256
43.3%
ValueCountFrequency (%)
4.11
0.2%
4.81
0.2%
6.41
0.2%
9.41
0.2%
101
0.2%
ValueCountFrequency (%)
77.71
0.2%
71.51
0.2%
70.52
0.3%
70.31
0.2%
70.21
0.2%

medication
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing338
Missing (%)57.2%
Memory size4.7 KiB
True
135 
False
118 
(Missing)
338 
ValueCountFrequency (%)
True135
 
22.8%
False118
 
20.0%
(Missing)338
57.2%
2021-02-12T10:50:18.381407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

medication_list
Categorical

HIGH CARDINALITY
MISSING

Distinct90
Distinct (%)65.7%
Missing454
Missing (%)76.8%
Memory size4.7 KiB
A
24 
H: PANTOPRAZOL
 
5
A,H: SMECTA
 
4
F
 
3
H;LORATADIN
 
3
Other values (85)
98 

Length

Max length55
Median length14
Mean length16.15328467
Min length1

Characters and Unicode

Total characters2213
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)53.3%

Sample

1st rowH; PIRACETAM, CIGENOL,VITAMIN C
2nd rowA, H:PIRACETAM
3rd rowA, H: PIRACETAM
4th rowH: PIRACETAM, OMEPRAZOL
5th rowH: PIRACETAM, OMEPRAZOLE
ValueCountFrequency (%)
A24
 
4.1%
H: PANTOPRAZOL5
 
0.8%
A,H: SMECTA4
 
0.7%
F3
 
0.5%
H;LORATADIN3
 
0.5%
H:GASTROLIUM, PANTOPRAZOLE,ACID TRANEXAMID3
 
0.5%
H: DIPHENHYDRAMIN HYDROCLORID2
 
0.3%
D2
 
0.3%
H: CIGENOL2
 
0.3%
H: DOXYCYCLIN,2
 
0.3%
Other values (80)87
 
14.7%
(Missing)454
76.8%
2021-02-12T10:50:18.608083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h40
 
15.7%
a32
 
12.5%
a,h11
 
4.3%
pantoprazol9
 
3.5%
piracetam5
 
2.0%
smecta5
 
2.0%
d5
 
2.0%
omeprazole5
 
2.0%
omeprazol4
 
1.6%
pantoprazole,acid4
 
1.6%
Other values (76)135
52.9%

Most occurring characters

ValueCountFrequency (%)
A290
 
13.1%
O167
 
7.5%
I137
 
6.2%
E134
 
6.1%
R126
 
5.7%
119
 
5.4%
T117
 
5.3%
H116
 
5.2%
L111
 
5.0%
N104
 
4.7%
Other values (22)792
35.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1894
85.6%
Other Punctuation197
 
8.9%
Space Separator119
 
5.4%
Decimal Number2
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A290
15.3%
O167
 
8.8%
I137
 
7.2%
E134
 
7.1%
R126
 
6.7%
T117
 
6.2%
H116
 
6.1%
L111
 
5.9%
N104
 
5.5%
M99
 
5.2%
Other values (14)493
26.0%
ValueCountFrequency (%)
,102
51.8%
:75
38.1%
;18
 
9.1%
.2
 
1.0%
ValueCountFrequency (%)
11
50.0%
01
50.0%
ValueCountFrequency (%)
119
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1894
85.6%
Common319
 
14.4%

Most frequent character per script

ValueCountFrequency (%)
A290
15.3%
O167
 
8.8%
I137
 
7.2%
E134
 
7.1%
R126
 
6.7%
T117
 
6.2%
H116
 
6.1%
L111
 
5.9%
N104
 
5.5%
M99
 
5.2%
Other values (14)493
26.0%
ValueCountFrequency (%)
119
37.3%
,102
32.0%
:75
23.5%
;18
 
5.6%
.2
 
0.6%
-1
 
0.3%
11
 
0.3%
01
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2213
100.0%

Most frequent character per block

ValueCountFrequency (%)
A290
 
13.1%
O167
 
7.5%
I137
 
6.2%
E134
 
6.1%
R126
 
5.7%
119
 
5.4%
T117
 
5.3%
H116
 
5.2%
L111
 
5.0%
N104
 
4.7%
Other values (22)792
35.8%

melaena
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
326 
True
 
1
(Missing)
264 
ValueCountFrequency (%)
False326
55.2%
True1
 
0.2%
(Missing)264
44.7%
2021-02-12T10:50:18.679686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

monocytes_percent
Real number (ℝ≥0)

MISSING

Distinct179
Distinct (%)53.4%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean16.49791045
Minimum2.4
Maximum60.9
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:18.761751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile6.64
Q110.7
median14.9
Q319.05
95-th percentile33.67
Maximum60.9
Range58.5
Interquartile range (IQR)8.35

Descriptive statistics

Standard deviation8.829551724
Coefficient of variation (CV)0.5351921234
Kurtosis5.174629379
Mean16.49791045
Median Absolute Deviation (MAD)4.2
Skewness1.941601728
Sum5526.8
Variance77.96098364
MonotocityNot monotonic
2021-02-12T10:50:18.878112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.66
 
1.0%
135
 
0.8%
14.85
 
0.8%
11.95
 
0.8%
13.95
 
0.8%
11.84
 
0.7%
9.44
 
0.7%
9.24
 
0.7%
11.24
 
0.7%
15.34
 
0.7%
Other values (169)289
48.9%
(Missing)256
43.3%
ValueCountFrequency (%)
2.41
0.2%
2.61
0.2%
4.61
0.2%
4.81
0.2%
5.41
0.2%
ValueCountFrequency (%)
60.91
0.2%
53.31
0.2%
52.31
0.2%
51.22
0.3%
47.91
0.2%

muscle_pain_level
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
527 
1.0
 
50
2.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan527
89.2%
1.050
 
8.5%
2.014
 
2.4%
2021-02-12T10:50:19.083828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:19.137309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan527
89.2%
1.050
 
8.5%
2.014
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
150
 
2.8%
214
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1581
89.2%
Decimal Number128
 
7.2%
Other Punctuation64
 
3.6%

Most frequent character per category

ValueCountFrequency (%)
064
50.0%
150
39.1%
214
 
10.9%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%
ValueCountFrequency (%)
.64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1581
89.2%
Common192
 
10.8%

Most frequent character per script

ValueCountFrequency (%)
.64
33.3%
064
33.3%
150
26.0%
214
 
7.3%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
150
 
2.8%
214
 
0.8%

neutrophils_percent
Real number (ℝ≥0)

MISSING

Distinct255
Distinct (%)76.3%
Missing257
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean40.20688623
Minimum10.5
Maximum90.2
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:19.228430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile15.965
Q126.875
median38.5
Q351.075
95-th percentile71.87
Maximum90.2
Range79.7
Interquartile range (IQR)24.2

Descriptive statistics

Standard deviation17.20698704
Coefficient of variation (CV)0.427961194
Kurtosis-0.4490802495
Mean40.20688623
Median Absolute Deviation (MAD)12.35
Skewness0.4591597173
Sum13429.1
Variance296.0804029
MonotocityNot monotonic
2021-02-12T10:50:19.350308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.84
 
0.7%
25.53
 
0.5%
31.53
 
0.5%
25.13
 
0.5%
58.93
 
0.5%
213
 
0.5%
35.73
 
0.5%
55.63
 
0.5%
38.63
 
0.5%
46.13
 
0.5%
Other values (245)303
51.3%
(Missing)257
43.5%
ValueCountFrequency (%)
10.51
0.2%
10.71
0.2%
11.12
0.3%
11.62
0.3%
11.81
0.2%
ValueCountFrequency (%)
90.21
0.2%
86.51
0.2%
81.21
0.2%
79.52
0.3%
78.32
0.3%

oedema_pulmonary
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
569 
True
 
19
(Missing)
 
3
ValueCountFrequency (%)
False569
96.3%
True19
 
3.2%
(Missing)3
 
0.5%
2021-02-12T10:50:19.429813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

parental_fluid_volume
Real number (ℝ≥0)

MISSING

Distinct59
Distinct (%)40.4%
Missing445
Missing (%)75.3%
Infinite0
Infinite (%)0.0%
Mean1095.291096
Minimum100
Maximum5850
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:19.512063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile332.25
Q1644.125
median1000
Q31000
95-th percentile2529.75
Maximum5850
Range5750
Interquartile range (IQR)355.875

Descriptive statistics

Standard deviation766.3389314
Coefficient of variation (CV)0.6996669052
Kurtosis11.53956114
Mean1095.291096
Median Absolute Deviation (MAD)120
Skewness2.80300881
Sum159912.5
Variance587275.3578
MonotocityNot monotonic
2021-02-12T10:50:19.633894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100061
 
10.3%
50020
 
3.4%
35003
 
0.5%
10602
 
0.3%
8202
 
0.3%
1002
 
0.3%
15002
 
0.3%
8802
 
0.3%
12502
 
0.3%
1871
 
0.2%
Other values (49)49
 
8.3%
(Missing)445
75.3%
ValueCountFrequency (%)
1002
0.3%
1501
0.2%
1871
0.2%
2001
0.2%
2501
0.2%
ValueCountFrequency (%)
58501
 
0.2%
36001
 
0.2%
35003
0.5%
30251
 
0.2%
28331
 
0.2%

pcr_dengue_interpretation
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Lab-confirmed Dengue
589 
Not Dengue
 
2

Length

Max length20
Median length20
Mean length19.96615905
Min length10

Characters and Unicode

Total characters11800
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLab-confirmed Dengue
2nd rowLab-confirmed Dengue
3rd rowLab-confirmed Dengue
4th rowLab-confirmed Dengue
5th rowLab-confirmed Dengue
ValueCountFrequency (%)
Lab-confirmed Dengue589
99.7%
Not Dengue2
 
0.3%
2021-02-12T10:50:19.840258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:19.898424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
dengue591
50.0%
lab-confirmed589
49.8%
not2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e1771
15.0%
n1180
 
10.0%
o591
 
5.0%
591
 
5.0%
D591
 
5.0%
g591
 
5.0%
u591
 
5.0%
L589
 
5.0%
a589
 
5.0%
b589
 
5.0%
Other values (9)4127
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9438
80.0%
Uppercase Letter1182
 
10.0%
Space Separator591
 
5.0%
Dash Punctuation589
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
e1771
18.8%
n1180
12.5%
o591
 
6.3%
g591
 
6.3%
u591
 
6.3%
a589
 
6.2%
b589
 
6.2%
c589
 
6.2%
f589
 
6.2%
i589
 
6.2%
Other values (4)1769
18.7%
ValueCountFrequency (%)
D591
50.0%
L589
49.8%
N2
 
0.2%
ValueCountFrequency (%)
-589
100.0%
ValueCountFrequency (%)
591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10620
90.0%
Common1180
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e1771
16.7%
n1180
11.1%
o591
 
5.6%
D591
 
5.6%
g591
 
5.6%
u591
 
5.6%
L589
 
5.5%
a589
 
5.5%
b589
 
5.5%
c589
 
5.5%
Other values (7)2949
27.8%
ValueCountFrequency (%)
591
50.1%
-589
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII11800
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1771
15.0%
n1180
 
10.0%
o591
 
5.0%
591
 
5.0%
D591
 
5.0%
g591
 
5.0%
u591
 
5.0%
L589
 
5.0%
a589
 
5.0%
b589
 
5.0%
Other values (9)4127
35.0%

pcr_dengue_load
Real number (ℝ≥0)

MISSING
ZEROS

Distinct52
Distinct (%)72.2%
Missing519
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean161132294.9
Minimum0
Maximum4080000000
Zeros21
Zeros (%)3.6%
Memory size4.7 KiB
2021-02-12T10:50:19.990076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median144500
Q37728571.75
95-th percentile595500000
Maximum4080000000
Range4080000000
Interquartile range (IQR)7728571.75

Descriptive statistics

Standard deviation678494348.1
Coefficient of variation (CV)4.210790572
Kurtosis28.62976761
Mean161132294.9
Median Absolute Deviation (MAD)144500
Skewness5.331735494
Sum1.160152523 × 1010
Variance4.603545804 × 1017
MonotocityNot monotonic
2021-02-12T10:50:20.120504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021
 
3.6%
439000001
 
0.2%
352001
 
0.2%
473000001
 
0.2%
43035711
 
0.2%
2770000001
 
0.2%
15800001
 
0.2%
74401
 
0.2%
53333331
 
0.2%
762001
 
0.2%
Other values (42)42
 
7.1%
(Missing)519
87.8%
ValueCountFrequency (%)
021
3.6%
17401
 
0.2%
30601
 
0.2%
35801
 
0.2%
47201
 
0.2%
ValueCountFrequency (%)
40800000001
0.2%
39300000001
0.2%
12559523811
0.2%
6010000001
0.2%
5910000001
0.2%

pcr_dengue_serotype
Categorical

MISSING

Distinct5
Distinct (%)0.9%
Missing24
Missing (%)4.1%
Memory size4.7 KiB
<LOD
160 
DENV-3
141 
DENV-4
105 
DENV-1
88 
DENV-2
73 

Length

Max length6
Median length6
Mean length5.435626102
Min length4

Characters and Unicode

Total characters3082
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<LOD
2nd row<LOD
3rd row<LOD
4th row<LOD
5th row<LOD
ValueCountFrequency (%)
<LOD160
27.1%
DENV-3141
23.9%
DENV-4105
17.8%
DENV-188
14.9%
DENV-273
12.4%
(Missing)24
 
4.1%
2021-02-12T10:50:20.354484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:20.429160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod160
28.2%
denv-3141
24.9%
denv-4105
18.5%
denv-188
15.5%
denv-273
12.9%

Most occurring characters

ValueCountFrequency (%)
D567
18.4%
E407
13.2%
N407
13.2%
V407
13.2%
-407
13.2%
<160
 
5.2%
L160
 
5.2%
O160
 
5.2%
3141
 
4.6%
4105
 
3.4%
Other values (2)161
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
68.4%
Dash Punctuation407
 
13.2%
Decimal Number407
 
13.2%
Math Symbol160
 
5.2%

Most frequent character per category

ValueCountFrequency (%)
D567
26.9%
E407
19.3%
N407
19.3%
V407
19.3%
L160
 
7.6%
O160
 
7.6%
ValueCountFrequency (%)
3141
34.6%
4105
25.8%
188
21.6%
273
17.9%
ValueCountFrequency (%)
<160
100.0%
ValueCountFrequency (%)
-407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
68.4%
Common974
31.6%

Most frequent character per script

ValueCountFrequency (%)
-407
41.8%
<160
 
16.4%
3141
 
14.5%
4105
 
10.8%
188
 
9.0%
273
 
7.5%
ValueCountFrequency (%)
D567
26.9%
E407
19.3%
N407
19.3%
V407
19.3%
L160
 
7.6%
O160
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3082
100.0%

Most frequent character per block

ValueCountFrequency (%)
D567
18.4%
E407
13.2%
N407
13.2%
V407
13.2%
-407
13.2%
<160
 
5.2%
L160
 
5.2%
O160
 
5.2%
3141
 
4.6%
4105
 
3.4%
Other values (2)161
 
5.2%

peptic_ulcer
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
520 
True
71 
ValueCountFrequency (%)
False520
88.0%
True71
 
12.0%
2021-02-12T10:50:20.484120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

petechiae
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
459 
True
129 
(Missing)
 
3
ValueCountFrequency (%)
False459
77.7%
True129
 
21.8%
(Missing)3
 
0.5%
2021-02-12T10:50:20.518757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pharyngeal_injection
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing266
Missing (%)45.0%
Memory size4.7 KiB
False
285 
True
40 
(Missing)
266 
ValueCountFrequency (%)
False285
48.2%
True40
 
6.8%
(Missing)266
45.0%
2021-02-12T10:50:20.554630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

platelets
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
137 
True
 
10
(Missing)
444 
ValueCountFrequency (%)
False137
 
23.2%
True10
 
1.7%
(Missing)444
75.1%
2021-02-12T10:50:20.598931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pleural_effusion
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
538 
True
 
50
(Missing)
 
3
ValueCountFrequency (%)
False538
91.0%
True50
 
8.5%
(Missing)3
 
0.5%
2021-02-12T10:50:20.633899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

MISSING

Distinct168
Distinct (%)50.1%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean103.6444776
Minimum9
Maximum687
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:20.713418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15.7
Q139.5
median72
Q3123
95-th percentile321.3
Maximum687
Range678
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation99.63932963
Coefficient of variation (CV)0.9613568608
Kurtosis6.474535472
Mean103.6444776
Median Absolute Deviation (MAD)40
Skewness2.272372525
Sum34720.9
Variance9927.99601
MonotocityNot monotonic
2021-02-12T10:50:20.856115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227
 
1.2%
637
 
1.2%
847
 
1.2%
306
 
1.0%
586
 
1.0%
696
 
1.0%
885
 
0.8%
175
 
0.8%
244
 
0.7%
374
 
0.7%
Other values (158)278
47.0%
(Missing)256
43.3%
ValueCountFrequency (%)
92
0.3%
102
0.3%
112
0.3%
123
0.5%
13.91
 
0.2%
ValueCountFrequency (%)
6871
0.2%
5321
0.2%
5032
0.3%
4391
0.2%
4332
0.3%

pregnant
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
580 
True
 
11
ValueCountFrequency (%)
False580
98.1%
True11
 
1.9%
2021-02-12T10:50:20.939695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pulse
Real number (ℝ≥0)

MISSING

Distinct55
Distinct (%)17.1%
Missing269
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean80.13043478
Minimum52
Maximum125
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:21.034539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60.1
Q172
median80
Q388
95-th percentile100
Maximum125
Range73
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.17685381
Coefficient of variation (CV)0.151962907
Kurtosis0.2604565193
Mean80.13043478
Median Absolute Deviation (MAD)8
Skewness0.4032746933
Sum25802
Variance148.2757687
MonotocityNot monotonic
2021-02-12T10:50:21.167623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8034
 
5.8%
7024
 
4.1%
7518
 
3.0%
8816
 
2.7%
9013
 
2.2%
7813
 
2.2%
7412
 
2.0%
8512
 
2.0%
659
 
1.5%
848
 
1.4%
Other values (45)163
27.6%
(Missing)269
45.5%
ValueCountFrequency (%)
521
 
0.2%
551
 
0.2%
561
 
0.2%
573
0.5%
582
0.3%
ValueCountFrequency (%)
1251
0.2%
1181
0.2%
1141
0.2%
1102
0.3%
1091
0.2%

rales_crackles
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
322 
True
 
5
(Missing)
264 
ValueCountFrequency (%)
False322
54.5%
True5
 
0.8%
(Missing)264
44.7%
2021-02-12T10:50:21.238062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rehydration
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
True
135 
False
 
12
(Missing)
444 
ValueCountFrequency (%)
True135
 
22.8%
False12
 
2.0%
(Missing)444
75.1%
2021-02-12T10:50:21.279210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

renal_disease
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing519
Missing (%)87.8%
Memory size4.7 KiB
False
71 
True
 
1
(Missing)
519 
ValueCountFrequency (%)
False71
 
12.0%
True1
 
0.2%
(Missing)519
87.8%
2021-02-12T10:50:21.314599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_distress
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
580 
True
 
8
(Missing)
 
3
ValueCountFrequency (%)
False580
98.1%
True8
 
1.4%
(Missing)3
 
0.5%
2021-02-12T10:50:21.351563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_rate
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)4.0%
Missing266
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean19.65846154
Minimum15
Maximum28
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:21.422371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q118
median20
Q320
95-th percentile23
Maximum28
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.797708788
Coefficient of variation (CV)0.09144707403
Kurtosis2.232740174
Mean19.65846154
Median Absolute Deviation (MAD)1
Skewness1.104787419
Sum6389
Variance3.231756885
MonotocityNot monotonic
2021-02-12T10:50:21.530179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20137
23.2%
18104
 
17.6%
2224
 
4.1%
1923
 
3.9%
2310
 
1.7%
248
 
1.4%
215
 
0.8%
164
 
0.7%
173
 
0.5%
253
 
0.5%
Other values (3)4
 
0.7%
(Missing)266
45.0%
ValueCountFrequency (%)
151
 
0.2%
164
 
0.7%
173
 
0.5%
18104
17.6%
1923
 
3.9%
ValueCountFrequency (%)
281
 
0.2%
262
 
0.3%
253
 
0.5%
248
1.4%
2310
1.7%

restlessness
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
324 
True
 
3
(Missing)
264 
ValueCountFrequency (%)
False324
54.8%
True3
 
0.5%
(Missing)264
44.7%
2021-02-12T10:50:21.601408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rhinitis
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing265
Missing (%)44.8%
Memory size4.7 KiB
False
321 
True
 
5
(Missing)
265 
ValueCountFrequency (%)
False321
54.3%
True5
 
0.8%
(Missing)265
44.8%
2021-02-12T10:50:21.639024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sbp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct63
Distinct (%)19.3%
Missing265
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean98.76380368
Minimum50
Maximum150
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:21.719650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q190
median100
Q3110
95-th percentile130
Maximum150
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.43246383
Coefficient of variation (CV)0.2068821073
Kurtosis-0.2560608251
Mean98.76380368
Median Absolute Deviation (MAD)10
Skewness-0.494139485
Sum32197
Variance417.4855781
MonotocityNot monotonic
2021-02-12T10:50:21.843557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10062
 
10.5%
11054
 
9.1%
12023
 
3.9%
9017
 
2.9%
13013
 
2.2%
6012
 
2.0%
708
 
1.4%
1058
 
1.4%
735
 
0.8%
955
 
0.8%
Other values (53)119
20.1%
(Missing)265
44.8%
ValueCountFrequency (%)
502
0.3%
551
 
0.2%
572
0.3%
583
0.5%
594
0.7%
ValueCountFrequency (%)
1502
0.3%
1451
0.2%
1361
0.2%
1351
0.2%
1332
0.3%

serology_interpretation
Categorical

MISSING

Distinct3
Distinct (%)4.2%
Missing519
Missing (%)87.8%
Memory size4.7 KiB
Probable Secondary
35 
Inconclusive
31 
Probable primary
6 

Length

Max length18
Median length16
Mean length15.25
Min length12

Characters and Unicode

Total characters1098
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInconclusive
2nd rowInconclusive
3rd rowInconclusive
4th rowInconclusive
5th rowInconclusive
ValueCountFrequency (%)
Probable Secondary35
 
5.9%
Inconclusive31
 
5.2%
Probable primary6
 
1.0%
(Missing)519
87.8%
2021-02-12T10:50:22.074367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:22.140229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
probable41
36.3%
secondary35
31.0%
inconclusive31
27.4%
primary6
 
5.3%

Most occurring characters

ValueCountFrequency (%)
o107
 
9.7%
e107
 
9.7%
n97
 
8.8%
c97
 
8.8%
r88
 
8.0%
b82
 
7.5%
a82
 
7.5%
l72
 
6.6%
P41
 
3.7%
41
 
3.7%
Other values (10)284
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter950
86.5%
Uppercase Letter107
 
9.7%
Space Separator41
 
3.7%

Most frequent character per category

ValueCountFrequency (%)
o107
11.3%
e107
11.3%
n97
10.2%
c97
10.2%
r88
9.3%
b82
8.6%
a82
8.6%
l72
7.6%
y41
 
4.3%
i37
 
3.9%
Other values (6)140
14.7%
ValueCountFrequency (%)
P41
38.3%
S35
32.7%
I31
29.0%
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1057
96.3%
Common41
 
3.7%

Most frequent character per script

ValueCountFrequency (%)
o107
10.1%
e107
10.1%
n97
 
9.2%
c97
 
9.2%
r88
 
8.3%
b82
 
7.8%
a82
 
7.8%
l72
 
6.8%
P41
 
3.9%
y41
 
3.9%
Other values (9)243
23.0%
ValueCountFrequency (%)
41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1098
100.0%

Most frequent character per block

ValueCountFrequency (%)
o107
 
9.7%
e107
 
9.7%
n97
 
8.8%
c97
 
8.8%
r88
 
8.0%
b82
 
7.5%
a82
 
7.5%
l72
 
6.6%
P41
 
3.7%
41
 
3.7%
Other values (10)284
25.9%

shock
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
568 
True
 
23
ValueCountFrequency (%)
False568
96.1%
True23
 
3.9%
2021-02-12T10:50:22.182440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

shock_resucitation
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
143 
True
 
4
(Missing)
444 
ValueCountFrequency (%)
False143
 
24.2%
True4
 
0.7%
(Missing)444
75.1%
2021-02-12T10:50:22.218167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_describe
Categorical

MISSING

Distinct5
Distinct (%)41.7%
Missing579
Missing (%)98.0%
Memory size4.7 KiB
MACULAR
5 
RECOVERY
3 
MACULOPAPULAR
2 
MACULER
1 
MACULAR
1 

Length

Max length13
Median length7.5
Mean length8.333333333
Min length7

Characters and Unicode

Total characters100
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st rowMACULOPAPULAR
2nd rowMACULOPAPULAR
3rd rowMACULAR
4th rowRECOVERY
5th rowMACULAR
ValueCountFrequency (%)
MACULAR5
 
0.8%
RECOVERY3
 
0.5%
MACULOPAPULAR2
 
0.3%
MACULER1
 
0.2%
MACULAR 1
 
0.2%
(Missing)579
98.0%
2021-02-12T10:50:22.404258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:22.470039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
macular6
50.0%
recovery3
25.0%
maculopapular2
 
16.7%
maculer1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
A19
19.0%
R15
15.0%
C12
12.0%
U11
11.0%
L11
11.0%
M9
9.0%
E7
 
7.0%
O5
 
5.0%
P4
 
4.0%
V3
 
3.0%
Other values (2)4
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter99
99.0%
Space Separator1
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
A19
19.2%
R15
15.2%
C12
12.1%
U11
11.1%
L11
11.1%
M9
9.1%
E7
 
7.1%
O5
 
5.1%
P4
 
4.0%
V3
 
3.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin99
99.0%
Common1
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
A19
19.2%
R15
15.2%
C12
12.1%
U11
11.1%
L11
11.1%
M9
9.1%
E7
 
7.1%
O5
 
5.1%
P4
 
4.0%
V3
 
3.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ValueCountFrequency (%)
A19
19.0%
R15
15.0%
C12
12.0%
U11
11.0%
L11
11.0%
M9
9.0%
E7
 
7.0%
O5
 
5.0%
P4
 
4.0%
V3
 
3.0%
Other values (2)4
 
4.0%

skin_flush
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
307 
True
281 
(Missing)
 
3
ValueCountFrequency (%)
False307
51.9%
True281
47.5%
(Missing)3
 
0.5%
2021-02-12T10:50:22.520027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
435 
True
153 
(Missing)
 
3
ValueCountFrequency (%)
False435
73.6%
True153
 
25.9%
(Missing)3
 
0.5%
2021-02-12T10:50:22.554816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

treatment_haemorrhage
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
146 
True
 
1
(Missing)
444 
ValueCountFrequency (%)
False146
 
24.7%
True1
 
0.2%
(Missing)444
75.1%
2021-02-12T10:50:22.594078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing82
Missing (%)13.9%
Memory size4.7 KiB
False
331 
True
178 
(Missing)
82 
ValueCountFrequency (%)
False331
56.0%
True178
30.1%
(Missing)82
 
13.9%
2021-02-12T10:50:22.627431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
560 
1.0
 
18
2.0
 
12
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan560
94.8%
1.018
 
3.0%
2.012
 
2.0%
3.01
 
0.2%
2021-02-12T10:50:22.784987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:22.839814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan560
94.8%
1.018
 
3.0%
2.012
 
2.0%
3.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1120
63.2%
a560
31.6%
.31
 
1.7%
031
 
1.7%
118
 
1.0%
212
 
0.7%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1680
94.8%
Decimal Number62
 
3.5%
Other Punctuation31
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
031
50.0%
118
29.0%
212
 
19.4%
31
 
1.6%
ValueCountFrequency (%)
n1120
66.7%
a560
33.3%
ValueCountFrequency (%)
.31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1680
94.8%
Common93
 
5.2%

Most frequent character per script

ValueCountFrequency (%)
.31
33.3%
031
33.3%
118
19.4%
212
 
12.9%
31
 
1.1%
ValueCountFrequency (%)
n1120
66.7%
a560
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1120
63.2%
a560
31.6%
.31
 
1.7%
031
 
1.7%
118
 
1.0%
212
 
0.7%
31
 
0.1%

wbc
Real number (ℝ≥0)

MISSING

Distinct92
Distinct (%)27.5%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean4.848656716
Minimum0.9
Maximum41.6
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:22.936933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.8
Q13
median4.3
Q35.7
95-th percentile9.6
Maximum41.6
Range40.7
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation3.140901171
Coefficient of variation (CV)0.6477879039
Kurtosis55.96581435
Mean4.848656716
Median Absolute Deviation (MAD)1.3
Skewness5.377124519
Sum1624.3
Variance9.865260166
MonotocityNot monotonic
2021-02-12T10:50:23.076810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.211
 
1.9%
3.810
 
1.7%
2.79
 
1.5%
3.69
 
1.5%
3.78
 
1.4%
3.98
 
1.4%
4.78
 
1.4%
2.68
 
1.4%
4.68
 
1.4%
4.98
 
1.4%
Other values (82)248
42.0%
(Missing)256
43.3%
ValueCountFrequency (%)
0.91
0.2%
11
0.2%
1.12
0.3%
1.32
0.3%
1.41
0.2%
ValueCountFrequency (%)
41.61
0.2%
15.21
0.2%
141
0.2%
13.91
0.2%
13.71
0.2%

weight
Real number (ℝ≥0)

Distinct32
Distinct (%)5.4%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean54.55612245
Minimum18
Maximum110
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:23.191736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35
Q145
median53
Q364
95-th percentile76
Maximum110
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.18142053
Coefficient of variation (CV)0.2599418707
Kurtosis2.758404468
Mean54.55612245
Median Absolute Deviation (MAD)8
Skewness0.8828774741
Sum32079
Variance201.1126882
MonotocityNot monotonic
2021-02-12T10:50:23.303528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5065
 
11.0%
4244
 
7.4%
5541
 
6.9%
5738
 
6.4%
5234
 
5.8%
7030
 
5.1%
4528
 
4.7%
6527
 
4.6%
5626
 
4.4%
6621
 
3.6%
Other values (22)234
39.6%
ValueCountFrequency (%)
187
 
1.2%
259
1.5%
3515
2.5%
3720
3.4%
3912
2.0%
ValueCountFrequency (%)
1108
1.4%
968
1.4%
809
1.5%
768
1.4%
748
1.4%

day_from_enrolment
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)7.5%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean-11.35204082
Minimum-4384
Maximum2916
Zeros72
Zeros (%)12.2%
Memory size4.7 KiB
2021-02-12T10:50:23.413566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4384
5-th percentile-5
Q1-1
median1
Q33
95-th percentile13.65
Maximum2916
Range7300
Interquartile range (IQR)4

Descriptive statistics

Standard deviation345.5254846
Coefficient of variation (CV)-30.43730112
Kurtosis139.4564457
Mean-11.35204082
Median Absolute Deviation (MAD)2
Skewness-9.062738667
Sum-6675
Variance119387.8605
MonotocityNot monotonic
2021-02-12T10:50:23.532286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
072
12.2%
170
11.8%
267
11.3%
-164
10.8%
358
9.8%
446
7.8%
-236
 
6.1%
-426
 
4.4%
-325
 
4.2%
-522
 
3.7%
Other values (34)102
17.3%
ValueCountFrequency (%)
-43841
0.2%
-43821
0.2%
-43801
0.2%
-921
0.2%
-881
0.2%
ValueCountFrequency (%)
29161
0.2%
18271
0.2%
7331
0.2%
3691
0.2%
631
0.2%

day_from_admission
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)7.1%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean-10.31122449
Minimum-4381
Maximum2918
Zeros72
Zeros (%)12.2%
Memory size4.7 KiB
2021-02-12T10:50:23.651260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4381
5-th percentile-4
Q10
median2
Q34
95-th percentile14.65
Maximum2918
Range7299
Interquartile range (IQR)4

Descriptive statistics

Standard deviation345.497666
Coefficient of variation (CV)-33.50694831
Kurtosis139.4380418
Mean-10.31122449
Median Absolute Deviation (MAD)2
Skewness-9.059465789
Sum-6063
Variance119368.6372
MonotocityNot monotonic
2021-02-12T10:50:23.768296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
072
12.2%
269
11.7%
368
11.5%
167
11.3%
458
9.8%
544
7.4%
-135
 
5.9%
-325
 
4.2%
-224
 
4.1%
-424
 
4.1%
Other values (32)102
17.3%
ValueCountFrequency (%)
-43812
0.3%
-43791
0.2%
-911
0.2%
-881
0.2%
-231
0.2%
ValueCountFrequency (%)
29181
0.2%
18281
0.2%
7341
0.2%
3701
0.2%
641
0.2%

Interactions

2021-02-12T10:49:12.740098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:12.807051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:12.870677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:12.940839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.010864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.074571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.136745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.203135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.279539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.351772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.431927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.496764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.569951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.641171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.715237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.791666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.858821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.930762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.004400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.075630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.149372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.218330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.295672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.373015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.454718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.538150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.599074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.657709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.728589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.793723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.854935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.916965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.972866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.038048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.107515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.170562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.236969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.302494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.367931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.433145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.500096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.568378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.631147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.695026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.764881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.828280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.895656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.960684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.019601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.083521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.150231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.209337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.274252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.339163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.405649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.472637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.528175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.588059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.652260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.717762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.781350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.838665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.904520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.970210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.028126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.093139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.158445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.223683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.292486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.353079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.415010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.474056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.534632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.590270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.655502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.717820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.787653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.858101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.924585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

study_nodateabdominal_pain_levelabdominal_tendernessagealbuminaltanemiaanorexiaantibioticsantibiotics_listascitesastasthmableedingbleeding_gumbleeding_nosebleeding_vaginalblood_fluidbody_temperaturebruisingchronic_hepatitiscolloidcolloid_descriptionconjunctival_injectioncoronary_heart_diseasecreatine_kinasecreatininecrystalloidcrystalloid_descriptiondbpdiabetesfluid_reason_otherfluid_reason_other_descriptiongenderhaematocrit_highhaematocrit_percenthaemoglobinheadache_levelheighthematemesishypertensioniggigg_interpretationigmigm_interpretationjoint_pain_levellethargyliver_palpationliver_sizelymphadenopathylymphadenopathy_specificationlymphocytes_percentmedicationmedication_listmelaenamonocytes_percentmuscle_pain_levelneutrophils_percentoedema_pulmonaryparental_fluid_volumepcr_dengue_interpretationpcr_dengue_loadpcr_dengue_serotypepeptic_ulcerpetechiaepharyngeal_injectionplateletspleural_effusionpltpregnantpulserales_cracklesrehydrationrenal_diseaserespiratory_distressrespiratory_raterestlessnessrhinitissbpserology_interpretationshockshock_resucitationskin_describeskin_flushskin_rashtreatment_haemorrhagevomitingvomiting_levelwbcweightday_from_enrolmentday_from_admission
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